AI

From Assets to Stories via the Google Cultural Institute Platform

Abstract

The Google Cultural Institute Platform scale system for ingesting, archiving, organizing, and interacting with digital assets of cultural material. This paper explains the components through which the platform contextualizes individual assets in order to enable storytelling. Contextualization is an inverse problem: given assets that are instances of cultural material, infer their precise context and use that as a way to support the storytelling process. The approach is based on three components: extraction, knowledge, and scale.

Extraction is the inference of context from two sources of information: explicitly provided metadata, and automatically extracted features. Knowledge is the use of a large refer- ence fact database for further contextualizing an asset based on its descriptors. And scale, achieved through global self-serve, enables massively expanded coverage of the knowledge database and crowdsource potential for metadata refinement.

Together these components sustain a storytelling framework and a compelling user experience that has the potential to become the largest repository of cultural information and coherent narrative in history.